Transfer Learning with Knowledge Distillation for Urban Localization Using LTE Signals

In urban areas with tall buildings and narrow streets, signal distortions from multipath and non-line-of-sight (NLOS) conditions significantly affect the localization accuracy using Long-Term Evolution (LTE) signals. To address these limitations and improve localization accuracy, we propose a teacher-student transfer learning framework based on graph neural network (GNN), utilizing LTE networks and receiver arrays. For the challenges of limited real data, our proposed model can effectively improve performance through fine-tuning with a generated synthetic dataset. Experimental findings validate the efficacy of our method, showcasing significant accuracy improvements of 41.3% and 53.3% for synthetic and real data, respectively, compared to existing techniques. Our approach outperforms conventional localization methods and alternative machine learning models, emphasizing its superior performance.